A Real‐World Evidence Framework for Optimizing Dosing in All Patients With COVID‐19

Peck, Richard; Weiner, Daniel; Cook, Jack; Powell, J. Robert

doi:10.1002/cpt.1922

Cited by 8 publications

(6 citation statements)

References 8 publications

(9 reference statements)

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“…Prospective studies are urgently needed for promoting effective and well tolerated drug treatments with sensitive CYP3A4 substrates in severe SARS-CoV-2 patients with high IL-6 levels associated with stage IIb and III disease, in agreement with what has already been suggested in a recent viewpoint claiming for action [28].…”

Section: Discussionsupporting

confidence: 72%

Comparative Population Pharmacokinetics of Darunavir in SARS-CoV-2 Patients vs. HIV Patients: The Role of Interleukin-6

et al. 2020

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Background Darunavir is an anti-HIV protease inhibitor repurposed for SARS-CoV-2 treatment. Objective The aim of this study was to assess the population pharmacokinetics of darunavir in SARS-CoV-2 patients compared with HIV patients. Methods Two separate models were created by means of a nonlinear mixed-effect approach. The influence of clinical covariates on each basic model was tested and the association of significant covariates with darunavir parameters was assessed at multivariate regression and classification and regression tree (CART) analyses. Monte Carlo simulation assessed the influence of covariates on the darunavir concentration versus time profile. Results A one-compartment model well-described darunavir concentrations in both groups. In SARS-CoV-2 patients (n = 30), interleukin (IL)-6 and body surface area were covariates associated with darunavir oral clearance (CL/F) and volume of distribution (V d), respectively; no covariates were identified in HIV patients (n = 25). Darunavir CL/F was significantly lower in SARS-CoV-2 patients compared with HIV patients (4.1 vs. 10.3 L/h; p < 0.001). CART analysis found that an IL-6 level of 18 pg/mL may split the SARS-CoV-2 population in patients with low versus high darunavir CL/F (mean ± standard deviation 3.47 ± 1.90 vs. 8.03 ± 3.24 L/h; proportion of reduction in error = 0.46). Median (interquartile range) darunavir CL/F was significantly lower in SARS-CoV-2 patients with IL-6 levels ≥ 18 pg/mL than in SARS-CoV-2 patients with IL-6 levels < 18 pg/mL or HIV patients (2.

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Section: Discussionsupporting

confidence: 72%

Comparative Population Pharmacokinetics of Darunavir in SARS-CoV-2 Patients vs. HIV Patients: The Role of Interleukin-6

et al. 2020

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“…A general framework for this continuous refinement and updating has already been described. 86,87 Ultimately, in addition to statements of the population "average" benefit from a new treatment, a future product label for a drug with a CATE algorithm might indicate how to estimate the expected benefit in each individual patient or even require use of the algorithm in order to target the treatment to those most likely to have the best response.…”

Section: Potential Applicationsmentioning

confidence: 99%

Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities

Curth,

Peck,

McKinney

et al. 2024

Clin Pharma and Therapeutics

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The use of data from randomized clinical trials to justify treatment decisions for real world patients is the current state of the art. It relies on the assumption that average treatment effects from the trial can be extrapolated to patients with personal and/or disease characteristics different from those treated in the trial. Yet, because of heterogeneity of treatment effects between patients and between the trial population and real‐world patients, this assumption may not be correct for many patients. Using machine learning to estimate the expected Conditional Average Treatment Effect (CATE) in individual patients from observational data offers the potential for more accurate estimation of the expected treatment effects in each patient based on their observed characteristics. In this review we discuss some of the challenges and opportunities for machine learning to estimate CATE, including ensuring data identification assumptions are met, managing covariate shift and learning without access to the true label of interest. We also discuss the potential applications as well as future work and collaborations needed to further improve identification and utilization of CATE estimates to increase patient benefit.This article is protected by copyright. All rights reserved.

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“…1, observational studies are important RWD as they provide information in uncontrolled settings generating data on a large and more diverse population. The large number of patients becoming ill in a very short period of time has led to an attempt to repurpose medications for the management of patients with COVID-19 [23]. For instance, the "tocilizumab in patients with severe COVID-19 pneumonia" (TESEO) study was an observational cohort study done in Italy and concluded that tocilizumab might reduce the risk of invasive me-chanical ventilation or death in patients with severe COVID-19 pneumonia.…”

Section: The Use Of Rwe During a Sanitary Crisis -The Example Of The mentioning

confidence: 99%

Real-world evidence and product development: Opportunities, challenges and risk mitigation

Naidoo¹,

Bouharati²,

Rambiritch

et al. 2021

Wien Klin Wochenschr

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Real-world evidence (RWE) is derived from real-world data (RWD) sources including electronic health records, claims data, registries (disease, product) and pragmatic clinical trials. The importance of RWE derived from RWD has been once again demonstrated during the coronavirus disease 2019 (COVID-19) pandemic, as it can improve patient care by complementing information obtained from traditional clinical trial programs. Additionally, RWE can generate insights into disease mechanisms, epidemiology, patient flows in and out of healthcare systems, and drivers and barriers to optimal clinical care in realworld settings. Identifying unmet medical needs is crucial as it often can inform which investigational new drugs enter clinical trial testing, and RWE studies from hospital settings have contributed substantial progress here. RWE can also optimize the design of clinical studies, inform benefit risk assessments and use networks of pragmatic studies to help with clinical trial feasibilities and eventual trial initiation. The challenges of RWD include data quality, reproducibility and accuracy which may affect validity. RWD

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A Real‐World Evidence Framework for Optimizing Dosing in All Patients With COVID‐19

Cited by 8 publications

References 8 publications

Comparative Population Pharmacokinetics of Darunavir in SARS-CoV-2 Patients vs. HIV Patients: The Role of Interleukin-6

Comparative Population Pharmacokinetics of Darunavir in SARS-CoV-2 Patients vs. HIV Patients: The Role of Interleukin-6

Using Machine Learning to Individualize Treatment Effect Estimation: Challenges and Opportunities

Real-world evidence and product development: Opportunities, challenges and risk mitigation

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